Public health quality frameworks: a scoping review
Bibliographic record
Abstract
Abstract Background Reliable performance frameworks and indicators are essential for understanding the ability of public health systems to meet their mandates and to encourage ongoing learning and quality improvement. While such frameworks exist in health care, they are not well established in public health. Methods We conducted a scoping review of indexed and grey literature to identify quality frameworks for public health systems in Canada and comparable countries. The search included documents published in English from 2012 to May 2022 and focused on countries with similar national contexts and public health systems to Canada. Articles that focused solely on the health care system, editorials, opinions, books, correspondence, or commentaries, and those that were highly focused on a specific area of public health work that was not generalizable, were excluded. The included studies underwent thematic analysis to identify common themes. Results The indexed literature search yielded 420 citations, none of which met the inclusion criteria. The grey literature search identified 1500 documents, four of which originated from national-level public health organizations in England, Wales, and the United States. The identified quality frameworks varied in their goals, reflecting the role of the organization producing the document. However, several themes common to the majority of frameworks emerged, including a skilled workforce, strong leadership, effective and timely service, equity, quality improvement, close partnerships, adequate resourcing, and innovation. Conclusion Although the study did not identify a commonly used framework or approach, it highlights major themes that can guide the development of a suite of indicators supporting structural and process enablers and community impact to measure and report on the quality of public health systems. Notably, the study emphasizes the importance of non-peer-reviewed literature in this field and underscores the need for more transparent documentation of framework and indicator development processes. Developing reliable performance frameworks and indicators that promote ongoing learning and quality improvement is crucial for public health systems to fulfill their mandate of promoting and protecting health in the population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.091 | 0.223 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.056 | 0.064 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".